Dysconnectivity between auditory-cognitive network associated with auditory GABA and glutamate levels in presbycusis patients

Accumulating studies suggest an interaction between presbycusis (PC) and cognitive impairment, which may be explained by the cognitive-ear link to a large extent. However, the neurophysiological mechanisms underlying this link are largely unknown. Here, 51 PC patients and 51 well-matched healthy controls were recruited. We combined resting-state functional MRI and edited magnetic resonance spectroscopy to investigate changes of intra- and inter-network functional connectivity and their relationships with auditory gamma-aminobutyric acid (GABA) and glutamate (Glu) levels and cognitive impairment in PC. Our study confirmed the plastic model of cognitive-ear link at the level of the large-scale brain network, including the dysconnectivity within high-order cognitive networks and between the auditory-cognitive network and overactivation between cognitive networks dependent on hearing loss, which was closely related to the cognitive impairment of PC patients. Moreover, GABA and Glu levels in the central auditory processing were abnormal in patients with PC. Importantly, reduction of GABA-mediated inhibition plays a crucial role in a dysconnectivity between the auditory-cognitive network, which may be neurochemical underpinnings of functional remodeling of cognitive-ear link in PC. Modulation of GABA neurotransmission may enable the development of new therapeutic strategies for the cognitive impairment of PC patients. Keywords: presbycusis, cognitive impairment, cognitive-ear link, GABA, resting-state networks.

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Reduced resting-state brain activity in the default mode network in children with (central) auditory processing disorders

Behavioral and Brain Functions ◽

10.1186/1744-9081-10-33 ◽

2014 ◽

Vol 10 (1) ◽

pp. 33 ◽

Cited By ~ 6

Author(s):

Agnieszka Pluta ◽

Tomasz Wolak ◽

Natalia Czajka ◽

Monika Lewandowska ◽

Katarzyna Cieśla ◽

...

Keyword(s):

Default Mode Network ◽

Resting State ◽

Auditory Processing ◽

Brain Activity ◽

Central Auditory Processing ◽

Auditory Processing Disorders ◽

Central Auditory Processing Disorders ◽

Default Mode

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Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

10.1101/2020.07.15.197590 ◽

2020 ◽

Author(s):

Pesoli Matteo ◽

Rucco Rosaria ◽

Liparoti Marianna ◽

Lardone Anna ◽

D’Aurizio Giula ◽

...

Keyword(s):

Sleep Deprivation ◽

Resting State ◽

Large Scale ◽

Brain Networks ◽

Brain Network ◽

Cognitive Tasks ◽

Functional Networks ◽

Long Sleep ◽

Before And After ◽

The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

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White-Matter Hyperintensity Load and Differences in Resting-State Network Connectivity Based on Mild Cognitive Impairment Subtype

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.737359 ◽

2021 ◽

Vol 13 ◽

Author(s):

Martina Vettore ◽

Matteo De Marco ◽

Claudia Pallucca ◽

Matteo Bendini ◽

Maurizio Gallucci ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

White Matter ◽

Resting State ◽

Large Scale ◽

Network Connectivity ◽

Functional Networks ◽

Frontoparietal Network ◽

Definition Of ◽

The Right

“Mild cognitive impairment” (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.

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Individual Variation in Brain Network Topology Predicts Emotional Intelligence

10.1101/275768 ◽

2018 ◽

Author(s):

Ling George ◽

Lee Ivy ◽

Guimond Synthia ◽

Lutz Olivia ◽

Tandon Neeraj ◽

...

Keyword(s):

Emotional Intelligence ◽

Social Cognition ◽

Network Topology ◽

Resting State ◽

Large Scale ◽

Psychotic Disorders ◽

Brain Connectivity ◽

Distance Matrix ◽

Resting State Fmri ◽

Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

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Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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Age-related changes in spatial and temporal features of resting state fMRI

10.1101/109181 ◽

2017 ◽

Author(s):

Shruti G. Vij ◽

Jason S. Nomi ◽

Dina R. Dajani ◽

Lucina Q. Uddin

Keyword(s):

Resting State ◽

Large Scale ◽

Spectral Composition ◽

Brain Network ◽

Functional Networks ◽

Functional Changes ◽

Age Related ◽

Temporal Features ◽

Functional Brain Network ◽

Age Related Changes

AbstractDevelopment and aging are associated with functional changes in the brain across the lifespan. These changes can be detected in spatial and temporal features of resting state functional MRI (rs-fMRI) data. Independent vector analysis (IVA) is a whole-brain multivariate approach that can be used to comprehensively assess these changes in spatial and temporal features. We present a multi-dimensional approach to assessing age-related changes in spatial and temporal features of statistically independent components identified by IVA in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale brain network configurations remain consistent throughout the lifespan, changes continue to occur in both local organization and in the spectral composition of these functional networks. We show that the spatial extent of functional networks decreases with age, but with no significant change in the peak functional loci of these networks. Additionally, we show differential age-related patterns across the frequency spectrum; lower frequency correlations decrease across the lifespan whereas higher-frequency correlations increase. These changes indicate an increasing stability of networks with age. In addition to replicating results from previous studies, the current results uncover new aspects of functional brain network changes across the lifespan that are frequency band-dependent.

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Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

Journal of Stroke ◽

10.5853/jos.2021.02376 ◽

2021 ◽

Vol 23 (3) ◽

pp. 297-311

Author(s):

Jae-Sung Lim ◽

Jae-Joong Lee ◽

Choong-Wan Woo

Keyword(s):

Cognitive Impairment ◽

Large Scale ◽

Imaging Techniques ◽

Brain Network ◽

Clinical Neurology ◽

Post Stroke ◽

Large Scale Networks ◽

High Level ◽

New Treatments ◽

The Brain

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

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EEG resting-state large-scale brain network dynamics are related to depressive symptoms

10.1101/619031 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alena Damborská ◽

Miralena I. Tomescu ◽

Eliška Honzírková ◽

Richard Barteček ◽

Jana Hořínková ◽

...

Keyword(s):

Depressive Symptoms ◽

Resting State ◽

Large Scale ◽

Rating Scale ◽

Depressive Symptomatology ◽

Network Dynamics ◽

Brain Network ◽

Temporal Characteristics ◽

Microstate Analysis ◽

Depressive Episodes

AbstractBackgroundThe few previous studies on resting-state EEG microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology.MethodsTo evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in patients with moderate to severe depression within bipolar affective disorder, depressive episode, and periodic depressive disorder, and in healthy controls.ResultsMicrostate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher symptomatology on the Montgomery-Åsberg Depression Rating Scale, a questionnaire validated as measuring severity of depressive episodes in patients with mood disorders, correlated with higher occurrence of microstate A (Spearman’s rank correlation, r = 0.70, p < 0.01).ConclusionOur results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. These findings suggest the utility of the microstate analysis approach in an objective depression assessment.

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Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

Network Neuroscience ◽

10.1162/netn_a_00129 ◽

2020 ◽

Vol 4 (2) ◽

pp. 448-466

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Resting State ◽

Large Scale ◽

Brain Network ◽

Generative Models ◽

Network Activity ◽

Fmri Data ◽

State Transitions ◽

Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

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